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Glama

Medicaid Intelligence

by io.github.pipeworx-io

Resolve Entity

resolve_entity
Read-onlyIdempotent

"What's the ticker for…" / "find the CIK for…" / "what's the LEI for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" — resolve a user-spoken NAME to the canonical/official identifiers other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (cross-source identity spine: 10-digit CIK + ticker + company_name from SEC EDGAR, legal-entity LEI from GLEIF with parent/ultimate-parent/children ownership when the LEI resolves, and security FIGI from OpenFIGI — by exact ticker map when a ticker is implied, and otherwise by name search, so NON-EQUITY instruments that never have a ticker (municipal and corporate bonds, notes, authority debt) DO resolve here; when a name matches more than one instrument it asserts nothing and returns figi_candidates to pick from, which is the correct answer to an issuer name that does not identify a single bond; every identifier is labelled with the source that established it, and an identifier that could NOT be resolved is stated explicitly under unresolved rather than omitted — accepts ticker, CIK, ISIN, or company name as input; an ISIN like "CH0038863350" resolves to the LEGAL ENTITY that issued the security via the GLEIF ISIN-to-LEI mapping, covering non-US issuers EDGAR cannot reach), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/concept/{rxcui} citation; accepts brand or generic name). LEI/FIGI enrichment degrades gracefully — if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return. Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valueYesFor company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., "ozempic", "metformin"). Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed ("NEW YORK ST DORM AUTH"), never the question's full noun phrase ("NEW YORK ST DORM AUTH revenue bonds"): the FIGI lookup matches instrument names, so trailing security-class words match nothing.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / value / description
      Previous value: -"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\"). Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed (\"NEW YORK ST DORM AUTH\"), never the question's full noun phrase (\"NEW YORK ST DORM AUTH revenue bonds\"): the FIGI lookup matches instrument names, so trailing security-class words match nothing."
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and idempotentHint=true, but the description adds substantial behavioral detail beyond that: graceful degradation when GLEIF/OpenFIGI is unavailable while EDGAR identifiers still return, cascading internal lookups across endpoints, coverage of non-equity instruments, and inclusion of ownership data. This goes well beyond what the annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long and dense, with an especially heavy parenthetical on company resolution that is hard to parse in one pass. However, it front-loads the core purpose and usage guidance, and nearly every sentence carries unique information about types, outputs, or failure behavior, so the verbosity is justified.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having no output schema, the description explains what each type returns (CIK/ticker/company_name/LEI/FIGI for company; RxCUI/ingredient/brand for drug), documents degraded behavior when upstream sources are down, and fully specifies input constraints. For a two-parameter tool, this is comprehensive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Even though schema coverage is 100%, the description adds critical semantic nuance: for company it clarifies accepted inputs (ticker, CIK, name) and for drug brand/generic names. It also includes a crucial warning for bonds to pass only the issuer name exactly as printed, never the full noun phrase, because the FIGI lookup matches instrument names. This materially improves invocation correctness beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with concrete example queries and states 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It clearly specifies the two supported types (company, drug) and the exact identifiers returned (CIK, ticker, LEI, FIGI, RxCUI), making the tool's purpose unambiguous and distinct from siblings like entity_profile or search_within.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a clear usage cue: 'Use FIRST whenever you have a name but need an ID,' and notes that one call replaces 2-3 manual lookups. However, it does not explicitly name sibling alternatives or state when not to use this tool (e.g., when an ID is already known and a profile is needed), so exclusion guidance is implicit rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.7/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve as broad data-query routers. The Polymarket tools (polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research) cover closely related trading/arbitrage functions with unclear boundaries. Even the Medicaid drug tools (medicaid_drug_state_market, medicaid_drug_trend, medicaid_drug_utilization) differ only subtly. Agents will struggle to choose correctly.

Naming Consistency3/5

Most tools use snake_case and descriptive phrases, but the patterns are inconsistent: some are verb_noun (generate_llms_txt, list_subscriptions), some are noun_heavy (medicaid_drug_state_market, entity_profile), and some are single verbs (forget, recall, remember). Versioned names like ask_pipeworx_beta and ask_pipeworx_grounded add to the mix. No dominant convention emerges.

Tool Count2/5

39 tools is far too many for a coherent set, especially for a server named 'Medicaid Intelligence.' A large portion of the tools (Polymarket arbitrage, npm dependency scanning, AI visibility checks, pipeworx meta-tools) are unrelated to the server's apparent purpose. The count feels bloated and unfocused.

Completeness3/5

For the Medicaid domain, the coverage is reasonable: drug utilization, enrollment, managed care, and plan market data are present. However, the server also tries to cover general data lookup, prediction markets, and entity research, making the overall surface feel scattered. Missing obvious Medicaid operations (e.g., provider data, claims, spending by state) suggest notable gaps if the stated purpose is Medicaid intelligence.

Resources